This document discusses using machine learning to analyze centralized cryptocurrency exchanges, which are important but remain opaque; it describes classifying exchange addresses and transactions through ensemble models to map deposit, withdrawal and hot/cold wallet flows; and highlights insights into exchange architectures, inflow/outflow patterns, and how understanding exchanges can benefit market analysis.

1. Understanding Crypto
Exchanges Using Data
Science

2. Agenda
2
❖ The challenges of centralized exchanges for crypto
analytics
❖ Why understanding centralized exchanges is a machine
learning problem?
❖ What machine learning can tell us about centralized
exchanges ?

3. Centralized exchanges are
extremely relevant to any
analysis of the crypto
market

4. Centralized Exchanges
4
❖ Are one of the most important components of the crypto
ecosystem
❖ Represent a significant portion of the daily trading volume
❖ Exchange inflows and outflows represent an important
metric of the behavior of the crypto ecosystem

5. But centralized exchanges
remain black boxes that
result incredibly hard to
analyze

6. Blockchain Analytics Nightmare
6
❖ Exchange trades are not always recorded in public
blockchains
❖ Volume manipulations
❖ Wallet anonymity
❖ Transaction composition patterns

7. A Gentle Introduction to
Crypto Exchanges

8. Used to
transfer
funds out
of the
exchange
Used to
transfer
funds into
an
exchange
Used to secure
the storage of
crypto-assets.
The main
interaction point
between
external parties
and an
exchange.
Hot
Wallets
Cold
Wallets
Withdrawal
Addresses
Deposit
Addresses

9. Analyzing crypto exchanges is a
machine learning problem…

10. Using machine learning to understand crypto
exchanges
Blockchain
Dataset
Machine
Learning Model
Exchange
Dataset

11. There are Different Methods for
Identifying Centralized
Exchanges Using Machine
Learning

12. The Clustering Way:
From this blockchain
dataset, what groups
look like exchanges?

13. The Classification Way:
Is this address an exchange or
not?

14. The Regression Way:
What’s the likelihood that
this address is an exchange?

15. How About Many Models?

16. Ensemble Learning
• A machine learning technique in
which multiple learners try to
master the same task and are
combined in a single master
model
• Ensemble models in machine
learning combine the decisions
from multiple models to
improve the overall
performance
• Work efficiently on diverse
datasets

17. What We Did…

18. Inside
IntoTheBlock’s
Exchange Classifier
ITB Exchange
Intelligence
Obtain
features from
the main
actors in
Ethereum
blockchain Create
ensemble
of models
Train on the
Ethereum
blockchain
Predict and
evaluate the
ensemble in the
Ethereum
blockchainGeneralize
the ensemble
to other
blockchains
Test and predict
on non-Ethereum
blockchains
Add blockchain
specific
heuristics
Optimize
model

19. What we Learned by Analyzing Crypto
Exchanges Using Machine Learning and
Fascinating Data Visualizations

20. INTRODUCING
Visualizing the complete transaction graph of an
exchange reveals incredible insights….

21. Visualizing the Anatomy of a Centralized Exchange

22. INTRODUCING
Transfers from many deposit addresses are
combined into single transactions into hot wallets…

23. Exchanges with Small Volume of Deposits

24. Exchanges with Large Number of Deposits

25. INTRODUCING
Exchanges implement different patterns to
transfer funds from deposit addresses….

26. Direct Transfers

27. Transfers Into Temporary Addresses

28. INTRODUCING
Exchanges also follow different architectures for
their withdrawal addresses…..

29. Low Volume Withdrawals

30. High Volume Withdrawals

31. INTRODUCINGExchanges also actively leverage UTXO
transactions….

32. UTXO in Action

33. INTRODUCINGAnalyzing patterns in some well-known
exchanges…

34. Single-Hot Wallet Architecture: Binance

35. Multi-Hot Wallet Architecture: Poloniex

36. Multi-Hot Wallet Architecture: BitStamp

37. INTRODUCINGWhat can we do once we understand centralized
exchanges…?

38. IntoTheBlock Signal: Exchange Inflows vs. Outflows

39. 3940
Some Challenges Worth Mentioning
❖ Continuous Training: There is not enough labeled datasets about
centralized exchanges for most cryptocurrencies.
❖ Architecture Changes: Exchanges regularly change their transaction
processing patterns.
❖ Privacy/Encryption: Blockchains like Monero or zCash obfuscate information
related to exchanges.

40. 4041
Summary
❖ ML First: Analyzing crypto exchanges is a machine learning problem
❖ More is Better: Combining many machine learning models into ensembles is
an efficient technique to tackle the classification of centralized exchanges
❖ The Core Architecture: The are four key components of centralized
exchanges: hot wallets, cold wallets, deposit addresses and withdrawal
addresses.
❖ Lots of Insights: The analysis of centralized exchanges reveals fascinating
insights that can provide an edge to researchers and traders in crypto
markets

41. INTRODUCING